Update app.py

app.py

@@ -17,7 +17,7 @@ def feifeimodload():
     device = "cuda" if torch.cuda.is_available() else "cpu"
     
     pipe = DiffusionPipeline.from_pretrained(
-        "black-forest-labs/FLUX.1-schnell", torch_dtype=dtype
+        "aifeifei798/DarkIdol-flux-v1", torch_dtype=dtype
     ).to(device)
     
     pipe.load_lora_weights(
@@ -25,20 +25,8 @@ def feifeimodload():
         adapter_name="feifei",
     )
 
-    pipe.set_adapters(
-        ["feifei"],
-        adapter_weights=[0.75],
-    )
-    
-    pipe.fuse_lora(
-        adapter_name=["feifei"],
-        lora_scale=1.0,
-    )
-
-    #pipe.enable_sequential_cpu_offload()
     pipe.vae.enable_slicing()
     pipe.vae.enable_tiling()
-    pipe.unload_lora_weights()
     torch.cuda.empty_cache()
     return pipe
     
@@ -47,15 +35,20 @@ MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 2048
 
 @spaces.GPU()
-def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, num_inference_steps=4, progress=gr.Progress(track_tqdm=True)):
+def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, num_inference_steps=4, num_feifei=0.35, progress=gr.Progress(track_tqdm=True)):
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
     generator = torch.Generator().manual_seed(seed)
-    # prompt = f"feifei, real model girl in real life, {prompt}, slight smile, Master of Light and Shadow."
-    # prompt = re.sub("young woman", "sexy feifei" ,prompt)
-    # prompt = re.sub("woman", "sexy feifei" ,prompt)
-    # prompt = re.sub("girl", "sexy feifei" ,prompt)
-    # prompt = re.sub("model", "sexy feifei" ,prompt)
+    
+    pipe.set_adapters(
+        ["feifei"],
+        adapter_weights=[num_feifei],
+    )
+    
+    pipe.fuse_lora(
+        adapter_name=["feifei"],
+        lora_scale=1.0,
+    )
     image = pipe(
             prompt = prompt,
             width = width,
@@ -140,7 +133,6 @@ with gr.Blocks(css=css) as demo:
             
             with gr.Row():
                 
-  
                 num_inference_steps = gr.Slider(
                     label="Number of inference steps",
                     minimum=1,
@@ -148,6 +140,15 @@ with gr.Blocks(css=css) as demo:
                     step=1,
                     value=4,
                 )
+                
+            with gr.Row():
+                num_feifei = gr.Slider(
+                    label="FeiFei",
+                    minimum=0,
+                    maximum=2,
+                    step=0.05,
+                    value=0.35,
+                )
         
         gr.Examples(
             examples = examples,
@@ -160,7 +161,7 @@ with gr.Blocks(css=css) as demo:
     gr.on(
         triggers=[run_button.click, prompt.submit],
         fn = infer,
-        inputs = [prompt, seed, randomize_seed, width, height, num_inference_steps],
+        inputs = [prompt, seed, randomize_seed, width, height, num_inference_steps, num_feifei],
         outputs = [result, seed]
     )